Digital channels offer organisations the ability to recognise customers and personalise experiences. What are the opportunities and associated risks? Rob recounts his journey at Sky including the ups, downs, lessons learned, and accomplishments. Rob's story is one of passion, politics, and technology, tied up with a pretty bow of data. If you're a Sky subscriber, you will be delighted and amazed by, and jealous of their ability to accumulate and coalesce the signals you send.

The RFM method allows you to obtain customers behavioural analysis in order to group them into clusters and to develop a marketing plan tailored to each specific market segment. We’ll explore how segmenting the market by when (recency), how often (frequency), and the amount spent (monetary) on a particular item or service can help you better understand your customers’ needs.

All is not roses in the world of online marketing and analytics. It's time to get into a serious discussion about the topics of the day. Working in teams, you'll create your arguments, debate your point of view and compete for top bragging rights at the front of the room. You'll be gnawing on chewy problems like personas, machine learning, multivariate testing, privacy, attribution, and more. This is a chance to sharpen your rhetorical skills, find out what others are thinking and - perchance - have your mind opened even wider.

Thousands of options for collecting data, and all of it is imperfect. Overwhelming complexity, mired in uncertainty, is the norm. No single source gives all the necessary information to any degree of trust, yet, we are tasked with using this data to evaluate investments in content. Hanna walks you through how to make the best of the imperfect, how to find context and trends amongst the numbers to make informed decisions with perfectly imperfect data, and how to identify a good idea using research and a range of metrics to score creative ideas to benefit SEO.

What does the future hold for Digital Analytics? Will new releases make existing tools more powerful or are they due to be replaced by new tools? Do tools even matter or does the future of Digital Analytics depend on people and their roles? Are there new ideas being floated around that will become common practice in organisations around the world? Is the industry about to die due to the privacy and data protection laws? This is Peter’s vision for the future...

Autotrader.co.uk is a huge marketplace: ~55 million searches and 85% of all car dealers in the UK advertise with us. Successful car dealers fear adapting to fit new high-speed tech demands of the millennial audience, and as a result are in danger of going out of business. That would be bad for Autotrader. Nick describes their contextualising exercise that illustrates how data can help them sell cars more quickly and improve their revenue and profit. See how data from multiple sources predicts the desirability of vehicle by region, how much dealers should pay for them, and how much they should sell them for. Like adding forensics to detective work.

Every business leader is grumbling over the shortage of highly skilled workers. There is good news. Today, everything is on-demand and borderless, including access to highly desired skills and professional services. The skills and talent we need are within our grasp if we operate differently, faster, more flexibly.

Experimentation is a framework to help chip away at problems and generate meaningful hypotheses to test. In a world of growing marketing costs, experiments can help you find new levels of growth. So why do so many experiments fail? You can give each experiment a fighting chance by avoiding improper experiment design, human error, incorrect KPI's, personal biases and cross-contamination. Beware: this talk may contain scenes of a statistics nature and may be unsuitable for younger audiences... or people who hate maths.

A modern marketer does not have to be a data scientist to benefit from the latest tools and techniques. But one does have to have a solid understanding of what they are, how they work, and who can help you make them work. Matt describes the variety of advanced methods available to the discerning marketer and reveals the seven secrets to making the most of mathematicians. From marketing data management to fundamental statistics, from testing essentials to hypothesis development, and from organizational readiness to agency partnership, Matt sets you up to succeed with everything from predictive analytics to machine learning.

The client seems happy enough just to collect the data. Our job is to make them understand what the data is for. Once they lose their data virginity, they won't want to stop. Doug describes personal scenarios where the awesome power of data was liberated once the data was actually used. Learn the tricks and techniques of the stealth change agent leading to that threshold moment in a data empowered career. Machine learning, data science, optimisation, professional jiu-jitsu and good old political persuasion are on the menu. Learn how to convince clients (internal or otherwise) to actually take your advice on board and do something other than just stare at a dashboard.

When you've pushed Excel & Tableau to their limits, you might need a data scientist. Matt has worked in e-commerce for over 10 years and is a statistician by trade. But when faced with a growing number of data types, a growing number of algorithmic options, and a growing competitive landscape, he brought in a specialist. Lissa, armed with a background in custom attribution and a proficiency in R and SQL, built a series of tools to collect, visualise, and action the huge amounts of hidden data behind modern eCommerce sites. Together, Lissa and Matt share the things we've learned along the way about tracking, briefing in new projects and keeping their momentum, and the pitfalls along the way in terms of process and data engineering. They impart some of the cool stuff you might like to investigate on your own site. And yes, Matt promises to start off with the 50 Shades of Grey story.

Lectures are wonderful, but nothing beats the up-close and personal conversation you get in a group of six to eight like-minded, data-savvy peers. These discussion sessions have been at the heart of our events since the start in 2002 and have always been overwhelmingly popular. Don't miss out on the best way to share experiences, gain new contacts, and get help from the people who really do feel your pain. Do NOT misuse this time on your phone. Be Here Now.

Using machine learning for cross channel attribution is no longer the preserve of major technology firms. Brands are increasingly developing their own algorithms to deepen customer relationships, driven by the wider availability of scaled cloud platforms and ML technologies. The case is strong for attribution for custom models, since every brand has unique marketing challenges, data sources and a unique relationship with their customers. Gabriel explores case studies from major brands who have used ML approaches for custom attribution. He covers the technical obstacles and resource limitations to making it happen, including key issues of cross device measurement, integrating offline and digital, and using attribution to optimise the marketing mix.

Is your customer experience design in the hands of a creative agency? How often does the data drive the design of those experiences? Predictive and machine learning techniques can help undercover hidden insights about your customers, their needs, aspirations and behaviours to fuel experiential design. Neil shares a culmination of wisdom gleaned from three decades of customer analysis and segmentation, promotions effectiveness, and category management.

Congratulations, you're an analyst! That means you're on the hook for making your organization more data-enhanced while actually wrangling the data itself. Donal lifts the lid on change management for a top-down view of stakeholder requirements and buy-in, capability and data audits, and project prioritization. Then, he pries up the floorboards for an under-the-hood look at some essential R resources and methods for the analyst. Yes, while you are courting the C-Suite, you can also get up and running with basic attribution (Markov Models), create user flow sankey charts using BigQuery, and build a competitive assessment of Brand presence on YouTube. This mix of high-level politics and deep-in-the-weeds coding is for everybody.

The first step in digital analytics is segmentation. How do different groups of people respond differently? Magda starts by explaining how to build a segmentation strategy and how to spot website behavioural patterns that uncover golden insights. She shares her methods of starting with general questions and digging deeper. Learn about segmentation based on site infrastructure, navigational facilitators, engagement metrics, demographics, devices, channels, search behaviour, and a lot more.

Capitalizing on the success of his 2017 book, "Artificial Intelligence for Marketing: Practical Applications", Jim looks at what Marketing will look like in 5 and 10 years. Come see what the world of marketing looks like when everything is a screen, all your data has been traded away for a pittance, and marketers have new tools with which to make age-old mistakes. Marketing is about to get weird. Hang on!

Yes, you’re a genius. Yes, all the answers are in your reports and dashboards. But do your stakeholders really understand? Do they even read look? Olaf is on a journey to make the figures and data more fun and understandable for his target stakeholders. Using storytelling, visuals, interaction and psychosocial insights, he gets executives to pay attention to stats and grasp core insights. Olaf shows how he translates dry data into attractive and personalized videos with real actors and different movie scenarios. He then gathers proof that this approach is hitting the mark with dynamic URL's to measure which stakeholders open their video and which parts they watch. You too can get your insights into the C-Suite and communicate the value of your work.

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